This paper develops a maximum likelihood based method for simultaneously performing multidimensional scaling and cluster analysis on two-way dominance or profile data. This MULTICLUS procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of...

The vast majority of existing multidimensional scaling (MDS) procedures devised for the analysis of paired comparison preference/choice judgments are typically based on either scalar product (i.e., vector) or unfolding (i.e., ideal-point) models. Such methods tend to ignore many of the essential...

This paper presents a new stochastic multidimensional scaling procedure for the analysis of three-mode, three-way pick any/ J data. The method provides either a vector or ideal-point model to represent the structure in such data, as well as “floating” model specifications (e.g., different...

This paper presents a new stochastic multidimensional scaling vector threshold model designed to analyze “pick any/ n ” choice data (e.g., consumers rendering buy/no buy decisions concerning a number of actual products). A maximum likelihood procedure is formulated to estimate a joint space...

We introduce an integrative tool, called a strategy map, for describing the nature of a given competitive environment. The approach can also suggest specific courses of actions for competing businesses in those environments. The strength of the method is its ability to capture and communicate...

This paper presents a new multidimensional scaling (MDS) methodology which operationalizes the Krumhansl (1978) distance-density model for the analysis of asymmetric proximity data. In Krumhansl's conceptualization, the symmetric Euclidean interbrand distances typically associated with the...